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| LIF Technology and ELM Algorithm Power Transformer Fault Diagnosis Research |
| YAN Peng-cheng1, 2, ZHANG Chao-yin2*, SUN Quan-sheng2, SHANG Song-hang2, YIN Ni-ni1, ZHANG Xiao-fei2 |
1. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
2. College of Electrical and Information Engineering, Anhui University of Technology, Huainan 232001, China
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Abstract The detection and analysis of power transformer oil is an effective method for power transformer fault diagnosis, and it is of great significance to quickly identify the oil sample of power transformer oil for power transformer fault diagnosis. The detection technology of conventional power transformer oil is mainly gas chromatography, which is complicated and not suitable for on-line detection, and it can’t find the fault hidden danger of transformer in time. A method for transformer fault diagnosis of laser-induced fluorescence spectroscopy (LIF) technology and extreme learning machine (ELM) algorithm is proposed. Four oil samples were collected: thermal fault oil, electrical fault oil, local moisture-affected oil and crude oil. The spectral data of different oil samples are obtained by using a laser generator to emit fluorescence. The spectral data are processed by MSC and SNV preprocessing algorithms to prevent noise and other factors. Subsequently, the use of KPCA and PCA dimension reduction, the main components are taken 5, KPCA processing shows that MSC pretreatment of the cumulative contribution rate of the highest, 99%, MSC pre-processed PCA model cumulative contribution rate is still more than 95%, Original-KPCA and Origin-PCA model cumulative contribution rate of less than 65%, you can find that the use of pretreatment model, cumulative contribution rate has increased. Finally, the data after the two dimensions are regression fit by ELM. Experiments show that KPCA and PCA are two kinds of dimensional reduction methods. The KPCA algorithm performs best, the processing time is short, and the reliability and efficiency of the model are improved. In the same KPCA dimension reduction mode, the fitting excellence R2 of the MSC-ELM model is 0.999 41, the mean square error MSE is 0.074%, and the SNV-ELM fit is 0.999 08, the mean square error MSE is 0.129%, The Original-ELM fitting excellence R2 is 0.996 95, the mean square error MSE is 0.399%, and the comparison can be found that MSC is better than SNV after processing. The MSC-KPCA-ELM model performs best, the prediction value is closer to the real value. The mean square root error is the smallest. The results show that the MSC-KPCA-ELM model, combined with laser-induced fluorescence spectroscopy technology, is more suitable for the rapid diagnosis of whether or not the power transformer has failed, which type of fault is accurately determined, and the operation safety of power equipment is guaranteed.
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Received: 2021-04-21
Accepted: 2021-06-08
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Corresponding Authors:
ZHANG Chao-yin
E-mail: 517806552@qq.com
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[1] WANG Wei-wang, LIU Ying, HE Jie-feng, et al(王威望, 刘 莹, 何杰峰, 等). High Voltage Engineering(高电压技术), 2020，(10): 3362.
[2] HUANG Da-rong, CHEN Chang-sha, SUN Guo-xi, et al(黄大荣, 陈长沙, 孙国玺, 等). Automation of Electric Power Systems(电力系统自动化), 2017, 41(12): 206.
[3] HE Zheng-you, LI Bo, LIAO Kai, et al(何正友, 李 波, 廖 凯, 等). Proceedings of the CSEE(中国电机工程学报), 2020, 40(19): 6193.
[4] PU Tian-jiao, QIAO Ji, HAN Xiao, et al(蒲天骄, 乔 骥, 韩 笑, 等). High Voltage Engineering(高电压技术), 2020, 46(2): 369.
[5] LIU Yun-peng, XU Zi-qiang, LI Gang, et al(刘云鹏, 许自强, 李 刚, 等). High Voltage Engineering(高电压技术), 2019, 45(2): 337.
[6] DAI Quan-min, QI Bo, LI Cheng-rong, et al(戴佺民, 齐 波, 李成榕, 等). Transactions of China Electrotechnical Society(电工技术学报), 2018, 33(11): 2613.
[7] LEI Lin, LAI Zhen-liang, ZHU Jia-long, et al(雷 霖, 赖真良, 朱家龙, 等). Power System Protection and Control(电力系统保护与控制), 2017，(17): 26.
[8] OUYANG Sen, MA Wen-jie(欧阳森, 马文杰). Electric Power Automation Equipment(电力自动化设备), 2018, 38(9): 21.
[9] MA Hong-zhong, GENG Zhi-hui, CHEN Kai, et al(马宏忠, 耿志慧, 陈 楷, 等). Automation of Electric Power Systems(电力系统自动化), 2013, 37(8): 89.
[10] XIAO Yan-wei, LOU Ping, JIANG Shui-zhong, et al(肖艳炜, 楼 平, 蒋水中, 等). Transformer(变压器), 2019, 56(4): 46.
[11] GONG Mao-fa, ZHANG Yan-pan, LIU Yan-ni, et al(公茂法, 张言攀, 柳岩妮, 等). Power System Protection and Control(电力系统保护与控制), 2015, 43(3): 113.
[12] LI En-wen, WANG Li-nong, SONG Bin, et al(李恩文, 王力农, 宋 斌, 等). Transactions of China Electrotechnical Society(电工技术学报), 2018, 33(19): 4594.
[13] WEN Quan, ZHOU Lu-bo, WANG Dong, et al(温 泉, 周录波, 王 栋, 等). Electrical Measurement & Instrumentation(电测与仪表), 2020, 57(13): 23.
[14] ZHOU Meng-ran, WANG Jin-guo, SONG Hong-ping, et al(周孟然, 王锦国, 宋红萍, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2020, 57(20): 203001.
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